Clustering-based point set registration method

A point set and clustering technology, applied in the field of image processing, can solve the problems of falling into local optimum and inaccurate correspondence estimation in the optimization process, so as to reduce the probability of local optimum and improve the efficiency of solution.

Pending Publication Date: 2022-01-07
ZHEJIANG SCI-TECH UNIV
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Problems solved by technology

A common problem in probabilistic non-rigid registration algorithms is that inaccurate correspondence estimates can easily lead the optimization process to fall into a local optimum

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  • Clustering-based point set registration method
  • Clustering-based point set registration method
  • Clustering-based point set registration method

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Embodiment Construction

[0038] 1 Probabilistic registration algorithm based on clustering

[0039] The key to the research of cluster-based probabilistic registration algorithm is to integrate cluster projection and local topology into the probabilistic registration framework based on Gaussian mixture model, so as to solve the lack of accuracy in the point set registration algorithm, and the optimization process is easy to fall into the local optimal question. First, cluster two given point sets separately; register the cluster center based on the point set registration framework of the Gaussian mixture model, then project the corresponding relationship of the cluster center to the point set, and finally perform registration to maintain the local structure . In the following content, the general point set registration framework based on the Gaussian mixture model is first introduced; on this basis, the clustering corresponding projection method is given; finally, the local structure topology preserv...

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Abstract

The invention discloses a clustering-based point set registration method, which comprises the following steps of: acquiring a reference point set and a target point set, respectively clustering the reference point set and the target point set to obtain a reference point set clustering center, a target point set clustering center and a clustering corresponding relation matrix, extending each clustering cluster pair in the clustering corresponding relation matrix into a point set pair of a point set corresponding to each clustering cluster pair to obtain a first objective function; fusing the displacement function model with the first target function to obtain a second target function, calculating the reference point set by adopting a local linear trapping method to obtain a cost function, fusing the cost function with the second target function, and then iterating the fused target function by adopting an expectation maximization algorithm to obtain a target function; and completing parameter optimization to obtain a final objective function. According to the method, point set registration can be accurately carried out.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a method for point set registration based on clustering. Background technique [0002] The point set registration problem is widely studied in computer vision, and the development of accurate point set registration algorithms has always been a research hotspot in the field of pattern recognition. The goal of point set registration is to find the correspondence between two sets of related points, or to restore the transformation relationship between two sets of points, so as to establish a one-to-one mapping between two sets of points. According to the different transformation methods, registration problems can be divided into two categories: rigid registration problems and non-rigid registration problems. The transformation method of rigid registration only considers translation, rotation and scaling. The transformation method of non-rigid registration is usually complex and dif...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23213
Inventor 刘申澳陈翰琦韩永华李景昂孙子昂马晨旭丁一凡刘奇坤陈璐
Owner ZHEJIANG SCI-TECH UNIV
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